Instructions to use christopheparisse/dream_80_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christopheparisse/dream_80_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="christopheparisse/dream_80_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("christopheparisse/dream_80_model") model = AutoModelForSequenceClassification.from_pretrained("christopheparisse/dream_80_model") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 4e77ac0
Training in progress epoch 2
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- tf_model.h5 +1 -1
.DS_Store
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README.md
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This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Train Accuracy: 0.
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- Epoch:
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## Model description
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### Framework versions
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This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.7120
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- Validation Loss: 0.6444
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- Train Accuracy: 0.6379
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- Epoch: 2
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## Model description
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|:----------:|:---------------:|:--------------:|:-----:|
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| 0.7502 | 0.7687 | 0.4310 | 0 |
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### Framework versions
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tf_model.h5
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